Drift compensation on electronic nose data for non-invasive diagnosis of prostate cancer by urine analysis

Prostate cancer risk stratification via eNose urine odor analysis: a preliminary report

Introduction

Prostate cancer (PCa) is known for its highly diverse clinical behavior, ranging from low-risk, slow-growing tumors to aggressive and life-threatening forms. To avoid over-treatment of low-risk PCa patients, it would be very important prior to any therapeutic intervention to appropriately classify subjects based on tumor aggressiveness. Unfortunately, there is currently no reliable test available for this purpose. The aim of the present study was to evaluate the ability of risk stratification of PCa subjects using an electronic nose (eNose) detecting PCa-specific volatile organic compounds (VOCs) in urine samples.

Methods

The study involved 120 participants who underwent diagnostic prostate biopsy followed by robot assisted radical prostatectomy (RARP). PCa risk was categorized as low, intermediate, or high based on the D'Amico risk classification and the pathological grade (PG) assessed after RARP. The eNose's ability to categorize subjects for PCa risk stratification was evaluated based on accuracy and recall metrics.

Results

The study population comprised 120 participants. When comparing eNose predictions with PG an accuracy of 79.2% (95%CI 70.8 - 86%) was found, while an accuracy of 74.2% (95%CI 65.4 - 81.7%) was found when compared to D'Amico risk classification system. Additionally, if compared low- versus -intermediate-/high-risk PCa, the eNose achieved an accuracy of 87.5% (95%CI 80.2-92.8%) based on PG or 90.8% (95%CI 84.2-95.3%) based on D'Amico risk classification. However, when using low-/-intermediate versus -high-risk PCa for PG, the accuracy was found to be 91.7% (95%CI 85.2-95.9%). Finally, an accuracy of 80.8% (95%CI72.6-87.4%) was found when compared with D'Amico risk classification.

Discussion

The findings of this study indicate that eNose may represent a valid alternative not only for early and non-invasive diagnosis of PCa, but also to categorize patients based on tumor aggressiveness. Further studies including a wider sample population will be necessary to confirm the potential clinical impact of this new technology.

Detection of urological cancers by the signature of organic volatile compounds in urine, from dogs to electronic noses

PURPOSE OF REVIEW

Urine volatile organic compound (VOC) testing for early detection of urological cancers is a minimally invasive and promising method. The objective of this review was to present the results of recently published work on this subject.

RECENT FINDINGS

Organic volatile compounds are produced through oxidative stress and peroxidation of cell membranes, and they are eliminated through feces, urine, and sweat. Studies looking for VOCs in urine for the diagnosis of urological cancers have mostly focused on bladder and prostate cancers. However, the number of patients included in the studies was small. The electronic nose was the most widely used means of detecting VOCs in urine for the detection of urological cancers. MOS sensors and pattern recognition machine learning were more used for the composition of electronic noses. Early detection of urological cancers by detection of VOCs in urine is a method with encouraging results with sensitivities ranging from 27 to 100% and specificities ranging from 72 to 94%.

SUMMARY

The olfactory signature of urine from patients with urological cancers is a promising biomarker for the early diagnosis of urological cancers. The electronic nose with its ability to recognize complex odors is an excellent alterative to canine diagnosis and analytical techniques. Nevertheless, additional research improving the technology of Enoses and the methodology of the studies is necessary for its implementation in daily clinical practice.

First-Order Individual Gas Sensors as Next Generation Reliable Analytical Instruments

It is conventionally expected that the performance of existing gas sensors may degrade in the field compared to laboratory conditions because (i) a sensor may lose its accuracy in the presence of chemical interferences and (ii) variations of ambient conditions over time may induce sensor-response fluctuations (i.e., drift). Breaking this status quo in poor sensor performance requires understanding the origins of design principles of existing sensors and bringing new principles to sensor designs. Existing gas sensors are single-output (e.g., resistance, electrical current, light intensity, etc.) sensors, also known as zero-order sensors (Karl Booksh and Bruce R. Kowalski, Analytical Chemistry, DOI: 10.1021/ac00087a718). Any zero-order sensor is undesirably affected by variable chemical background and sensor drift that cannot be distinguished from the response to an analyte. To address these limitations, we are developing multivariable gas sensors with independent responses, which are first-order analytical instruments. Here, we demonstrate self-correction against drift in two types of first-order gas sensors that operate in different portions of the electromagnetic spectrum. Our radiofrequency sensors utilize dielectric excitation of semiconducting metal oxide materials on the shoulder of their dielectric relaxation peak and achieve self-correction of the baseline drift by operation at several frequencies. Our photonic sensors utilize nanostructured sensing materials inspired by Morpho butterflies and achieve self-correction of the baseline drift by operation at several wavelengths. These principles of self-correction for drift effects in first-order sensors open opportunities for diverse emerging monitoring applications that cannot afford frequent periodic maintenance that is typical of traditional analytical instruments.

Early Detection of Prostate Cancer: The Role of Scent

Prostate cancer (PCa) represents the cause of the second highest number of cancer-related deaths worldwide, and its clinical presentation can range from slow-growing to rapidly spreading metastatic disease. As the characteristics of most cases of PCa remains incompletely understood, it is crucial to identify new biomarkers that can aid in early detection. Despite the prostate-specific antigen serum (PSA) levels, prostate biopsy, and imaging representing the actual gold-standard for diagnosing PCa, analyzing volatile organic compounds (VOCs) has emerged as a promising new frontier. We and other authors have reported that highly trained dogs can recognize specific VOCs associated with PCa with high accuracy. However, using dogs in clinical practice has several limitations. To exploit the potential of VOCs, an electronic nose (eNose) that mimics the dog olfactory system and can potentially be used in clinical practice was designed. To explore the eNose as an alternative to dogs in diagnosing PCa, we conducted a systematic literature review and meta-analysis of available studies. PRISMA guidelines were used for the identification, screening, eligibility, and selection process. We included six studies that employed trained dogs and found that the pooled diagnostic sensitivity was 0.87 (95% CI 0.86–0.89; I2, 98.6%), the diagnostic specificity was 0.83 (95% CI 0.80–0.85; I2, 98.1%), and the area under the summary receiver operating characteristic curve (sROC) was 0.64 (standard error, 0.25). We also analyzed five studies that used an eNose to diagnose PCa and found that the pooled diagnostic sensitivity was 0.84 (95% CI, 0.80–0.88; I2, 57.1%), the diagnostic specificity was 0.88 (95% CI, 0.84–0.91; I2, 66%), and the area under the sROC was 0.93 (standard error, 0.03). These pooled results suggest that while highly trained dogs have the potentiality to diagnose PCa, the ability is primarily related to olfactory physiology and training methodology. The adoption of advanced analytical techniques, such as eNose, poses a significant challenge in the field of clinical practice due to their growing effectiveness. Nevertheless, the presence of limitations and the requirement for meticulous study design continue to present challenges when employing eNoses for the diagnosis of PCa.

Quantitation of ethanol in UTI assay for volatile organic compound detection by electronic nose using the validated headspace GC-MS method

Disease detection through gas analysis has long been the topic of many studies because of its potential as a rapid diagnostic technique. In particular, the pathogens that cause urinary tract infection (UTI) have been shown to generate different profiles of volatile organic compounds, thus enabling the discrimination of causative agents using an electronic nose. While past studies have performed data collection on either agar culture or jellified urine culture, this study measures the headspace volume of liquid urine culture samples. Evaporation of the liquid and the presence of background compounds during electronic nose (e-nose) device operation could introduce variability to the collected data. Therefore, a headspace gas chromatography-mass spectrometry method was developed and validated for quantitating ethanol in the headspace of the urine samples. By leveraging the new method to characterize the sample stability during e-nose measurement, it was revealed that ethanol concentration dropped more than 15% after only three measurement cycles, which equal 30 minutes for this study. It was further shown that by using only data within the first three cycles, better accuracies for between-day classification were achieved, which was 73.7% and 97.0%, compared to using data from within the first nine cycles, which resulted in 65.0% and 81.1% accuracies. Therefore, the newly developed method provides better quality control for data collection, paving ways for the future establishment of a training data library for UTI.

Determination of Odor Air Quality Index (OAQII) Using Gas Sensor Matrix

This article presents a new way to determine odor nuisance based on the proposed odor air quality index (OAQII), using an instrumental method. This indicator relates the most important odor features, such as intensity, hedonic tone and odor concentration. The research was conducted at the compost screening yard of the municipal treatment plant in Central Poland, on which a self-constructed gas sensor array was placed. It consisted of five commercially available gas sensors: three metal oxide semiconductor (MOS) chemical sensors and two electrochemical ones. To calibrate and validate the matrix, odor concentrations were determined within the composting yard using the field olfactometry technique. Five mathematical models (e.g., multiple linear regression and principal component regression) were used as calibration methods. Two methods were used to extract signals from the matrix: maximum signal values from individual sensors and the logarithm of the ratio of the maximum signal to the sensor baseline. The developed models were used to determine the predicted odor concentrations. The selection of the optimal model was based on the compatibility with olfactometric measurements, taking the mean square error as a criterion and their accordance with the proposed OAQII. For the first method of extracting signals from the matrix, the best model was characterized by RMSE equal to 8.092 and consistency in indices at the level of 0.85. In the case of the logarithmic approach, these values were 4.220 and 0.98, respectively. The obtained results allow to conclude that gas sensor arrays can be successfully used for air quality monitoring; however, the key issues are data processing and the selection of an appropriate mathematical model.

Accuracy of a new electronic nose for prostate cancer diagnosis in urine samples

Objective

To evaluate the accuracy of a new electronic nose to recognize prostate cancer in urine samples.

Methods

A blind, prospective study on consecutive patients was designed. Overall, 174 subjects were included in the study: 88 (50.6%) in prostate cancer group, and 86 (49.4%) in control group. Electronic nose performance for prostate cancer was assessed using sensitivity and specificity. The diagnostic accuracy of electronic nose was reported as area under the receiver operating characteristic curve.

Results

The electronic nose in the study population reached a sensitivity 85.2% (95% confidence interval 76.1–91.9; 13 false negatives out of 88), a specificity 79.1% (95% confidence interval 69.0–87.1; 18 false positives out of 86). The accuracy of the electronic nose represented as area under the receiver operating characteristic curve 0.821 (95% confidence interval 0.764–0.879).

Conclusions

The diagnostic accuracy of electronic nose for recognizing prostate cancer in urine samples is high, promising and susceptible to supplemental improvement. Additionally, further studies will be necessary to design a clinical trial to validate electronic nose application in diagnostic prostate cancer nomograms.

An Experimental Apparatus for E-Nose Breath Analysis in Respiratory Failure Patients

Background: Non-invasive, bedside diagnostic tools are extremely important for tailo ring the management of respiratory failure patients. The use of electronic noses (ENs) for exhaled breath analysis has the potential to provide useful information for phenotyping different respiratory disorders and improving diagnosis, but their application in respiratory failure patients remains a challenge. We developed a novel measurement apparatus for analysing exhaled breath in such patients. Methods: The breath sampling apparatus uses hospital medical air and oxygen pipeline systems to control the fraction of inspired oxygen and prevent contamination of exhaled gas from ambient Volatile Organic Compounds (VOCs) It is designed to minimise the dead space and respiratory load imposed on patients. Breath odour fingerprints were assessed using a commercial EN with custom MOX sensors. We carried out a feasibility study on 33 SARS-CoV-2 patients (25 with respiratory failure and 8 asymptomatic) and 22 controls to gather data on tolerability and for a preliminary assessment of sensitivity and specificity. The most significant features for the discrimination between breath-odour fingerprints from respiratory failure patients and controls were identified using the Boruta algorithm and then implemented in the development of a support vector machine (SVM) classification model. Results: The novel sampling system was well-tolerated by all patients. The SVM differentiated between respiratory failure patients and controls with an accuracy of 0.81 (area under the ROC curve) and a sensitivity and specificity of 0.920 and 0.682, respectively. The selected features were significantly different in SARS-CoV-2 patients with respiratory failure versus controls and asymptomatic SARS-CoV-2 patients (p < 0.001 and 0.046, respectively). Conclusions: the developed system is suitable for the collection of exhaled breath samples from respiratory failure patients. Our preliminary results suggest that breath-odour fingerprints may be sensitive markers of lung disease severity and aetiology.